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CrystalLLM is presented to decouple large-scale execution from the need to access large clusters, enabling engineers to run and observe ranks of interest under faithful large-scale behavior using only a few GPUs.
This work argues for co-design, that facilities should treat agents as first-class principals where agents become facility-aware tenants, and outlines the resulting challenges and opportunities in compute, storage, agent memory, and safety.
This work studies production traces from two companies and evaluates 14 eviction algorithms across HBM-constrained and large memory-pool settings to show that effective prefix-cache management should retain recency as its foundation while selectively adding quick demotion for one-hit prefixes.
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